Computation of the ARL for CUSUM-\(S^2\) schemes
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Publication:1010385
DOI10.1016/j.csda.2005.09.015zbMath1157.62562OpenAlexW2089201452MaRDI QIDQ1010385
Publication date: 6 April 2009
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2005.09.015
collocationFredholm integral equation of the second kindcontrol chartsErlang distributionaverage run lengthmonitoring variance
Related Items (11)
A Design ofsControl Charts with a Combined Double Sampling and Variable Sampling Interval Scheme ⋮ A nonparametric exponentially weighted moving average signed-rank chart for monitoring location ⋮ Reliability and sensitivity comparisons and average run lengths of CUSUM scale chart ⋮ Evaluation of exponentially weighted moving variance control chart subject to linear drifts ⋮ Likelihood ratio test-based chart for monitoring the process variability ⋮ New EWMA control charts for monitoring process dispersion ⋮ Optimal Cumulative Sum Charting Procedures Based on Kernel Densities ⋮ Computation of the run-length percentiles of CUSUM control charts under changes in variances ⋮ Design of adaptivescontrol charts ⋮ Adaptive threshold computation for CUSUM-type procedures in change detection and isolation problems ⋮ Optimal Sequential Surveillance for Finance, Public Health, and Other Areas
Uses Software
Cites Work
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- Decision theoretic optimality of the cusum procedure
- Optimal stopping times for detecting changes in distributions
- Integral equations. Theory and numerical treatment
- Algorithm AS 258: Average Run Lengths for Cumulative Sum Schemes
- The Distribution of the Run Length of One-Sided CUSUM Procedures for Continuous Random Variables
- Bounds for the Distribution of the Run Length of One-Sided and Two-Sided CUSUM Quality Control Schemes
- A a comparison of the markov chain and the integral equation approaches for evaluating the run length distribution of quality control charts
- Some Omnibus Exponentially Weighted Moving Average Statistical Process Monitoring Schemes
- Cusum procedure for monitoring variability
- Monitoring Variance Components
- Evaluating properties of variable sampling interval control charts
- Reconciling the Integral Equation and Markov Chain Approaches for Computing EWMA Average Run Lengths
- The performance of control charts for monitoring process variation
- Procedures for Reacting to a Change in Distribution
- An approach to the probability distribution of cusum run length
- CONTINUOUS INSPECTION SCHEMES
- Quasi-stationarity of CUSUM schemes for Erlang distributions.
- An accurate algorithm to compute the run length probability distribution, and its convolutions, for a cusum chart to control normal mean
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